The reviewed record of science sign in
Pith

arxiv: 2607.02198 · v1 · pith:72RTV65Y · submitted 2026-07-02 · cs.HC · cs.AI

What Types of Human-AI Teams Exist?

Nathan Hughes , Ibrahim Habli This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-07-03 06:02 UTCgrok-4.3pith:72RTV65Yrecord.jsonopen to challenge →

classification cs.HC cs.AI
keywords human-AI teamingteam typespsychological taxonomiescategorizationhuman-AI collaborationteam clustersresearch synthesis
0
0 comments X

The pith

Human-AI teams studied in research fall into five distinct types drawn from psychological taxonomies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reviews 53 studies and sorts them into five clusters using psychological models of human teaming: AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity. Each cluster shows a unique mix of team-level traits, so the single label human-AI teaming covers several different arrangements. A sympathetic reader would conclude that findings from one cluster cannot be assumed to hold for the others. The authors supply a reporting checklist and guidance to make these distinctions clearer in future work.

Core claim

Analysis of the literature shows five separate types of human-AI teams, each defined by a distinct combination of holistic team-level characteristics taken from psychological taxonomies, indicating that the term human-AI team covers multiple disparate structures.

What carries the argument

The five-cluster categorization of papers based on psychological taxonomies of teaming, where each cluster is identified by its particular set of team-level characteristics.

If this is right

  • Insights drawn from studies of one cluster cannot be assumed to transfer to the other four.
  • Papers must specify which cluster their human-AI team belongs to for findings to be interpretable.
  • A standardized checklist can help authors report the relevant team-level traits consistently.
  • Synthesis across the field requires separating the clusters rather than pooling all studies together.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Empirical studies could check whether observed human-AI interactions actually align with these five psychological categories.
  • The mapping may point toward the value of developing team taxonomies built specifically around AI capabilities rather than borrowed human ones.
  • AI system design choices could be made differently depending on which of the five team types is intended.

Load-bearing premise

That psychological taxonomies of human teaming can be applied directly to human-AI teams without substantial modification.

What would settle it

A follow-up review or empirical test that finds most human-AI team studies exhibit characteristics that cut across the five clusters or that the clusters fail to separate the papers meaningfully.

read the original abstract

Human-AI teaming has received increasing attention in the literature. However, the range of studies conducted in multiple domains make it difficult to understand what types of teams are being studied, and in what ways are they similar/different from one another. In this study, we analyse 53 papers on human-AI teams and categorise them into five main clusters based on psychological taxonomies of teaming; AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity. Each cluster represents a unique combination of holistic team-level characteristics, indicating there are multiple disparate team types studied under the same definition. In turn, this raises the question of whether insights are truly transferable between papers. We conclude with guidance on how to identify the types of human-AI teams studied, a checklist for reporting a human-AI team in research work, and ways in which the field can be further synthesised.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper reviews 53 studies on human-AI teaming and categorizes them into five clusters (AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, Group Equanimity) derived from psychological taxonomies of human teaming. It claims these clusters capture distinct holistic team-level characteristics, questions the transferability of insights across the literature, and concludes with reporting guidance and a checklist.

Significance. A validated taxonomy that distinguishes team types in human-AI research could aid synthesis and improve comparability across studies; the checklist component offers a concrete contribution if the underlying clusters prove reproducible.

major comments (3)
  1. [Abstract / Methods] Abstract and Methods: The central categorization into five clusters is described as 'based on psychological taxonomies of teaming' with no reported inclusion/exclusion criteria for the 53 papers, no inter-rater reliability statistics, and no sensitivity analysis to alternative coding schemes or taxonomies; this directly undermines the claim that the clusters represent 'disparate team types' rather than post-hoc groupings.
  2. [Abstract] Abstract: The mapping of constructs such as 'equanimity' and 'dependency' from human psychological taxonomies to AI agents is asserted without shown operationalization, adaptation, or empirical check that the resulting clusters differ on any measurable team-level variable beyond author judgment.
  3. [Results / Discussion] Results / Discussion: No table or section presents the distribution of papers across clusters, example codings, or quantitative evidence (e.g., cluster separation metrics) that the five types are distinct rather than overlapping or arbitrary.
minor comments (2)
  1. [Abstract] The abstract states 'we analyse 53 papers' but provides no citation list or supplementary table identifying the papers; this should be added for reproducibility.
  2. [Introduction] Terminology such as 'holistic team-level characteristics' is used without a precise definition or reference to the source taxonomies.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight opportunities to improve the transparency of our methods and presentation. We address each major comment below and indicate where revisions will be made.

read point-by-point responses

  1. Referee: [Abstract / Methods] Abstract and Methods: The central categorization into five clusters is described as 'based on psychological taxonomies of teaming' with no reported inclusion/exclusion criteria for the 53 papers, no inter-rater reliability statistics, and no sensitivity analysis to alternative coding schemes or taxonomies; this directly undermines the claim that the clusters represent 'disparate team types' rather than post-hoc groupings.

    Authors: We agree that explicit details on paper selection strengthen the work. The 53 papers were identified via a systematic search using terms such as 'human-AI teaming' and 'human-AI collaboration' in relevant databases, with inclusion focused on studies describing team-level interactions. We will add a Methods section detailing the search strategy and inclusion/exclusion criteria. The categorization followed an iterative, consensus-based process among authors grounded in the cited psychological taxonomies rather than independent multi-rater coding, so inter-rater reliability metrics do not apply; we will note this rationale. Sensitivity analyses to alternative taxonomies lie outside the current scope but can be listed as a limitation. The clusters are not post-hoc but derive directly from combinations specified in the source taxonomies. revision: partial

  2. Referee: [Abstract] Abstract: The mapping of constructs such as 'equanimity' and 'dependency' from human psychological taxonomies to AI agents is asserted without shown operationalization, adaptation, or empirical check that the resulting clusters differ on any measurable team-level variable beyond author judgment.

    Authors: The constructs are adapted conceptually from established human-team taxonomies, with 'dependency' referring to the extent the AI is required for task completion as described in each paper and 'equanimity' referring to balanced mutual influence. We will add an explicit section or table defining these adaptations and how they were applied to the reviewed papers. No new empirical measurements of team-level variables are provided, as this is a review synthesizing existing literature rather than a primary study; the distinctions rest on the differing profiles of characteristics reported across the papers. revision: yes

  3. Referee: [Results / Discussion] Results / Discussion: No table or section presents the distribution of papers across clusters, example codings, or quantitative evidence (e.g., cluster separation metrics) that the five types are distinct rather than overlapping or arbitrary.

    Authors: We agree that a summary table would improve clarity and will add one in the Results section listing the number of papers per cluster together with representative examples and the defining characteristics for each. Quantitative cluster-separation metrics (e.g., silhouette scores) are not applicable, as the categorization is a theory-driven qualitative mapping to psychological frameworks rather than algorithmic clustering of numerical data. Distinctness is argued via the non-overlapping combinations of team characteristics drawn from the taxonomies. revision: partial

Circularity Check

0 steps flagged

No circularity: external literature categorization using independent taxonomies

full rationale

The paper conducts a qualitative synthesis of 53 existing studies on human-AI teams, assigning them to five clusters (AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, Group Equanimity) by applying psychological taxonomies of teaming. No equations, parameter fitting, or predictive derivations are present. The clusters are not defined in terms of themselves, nor do any results reduce by construction to fitted inputs or self-citations. The taxonomies invoked are external psychological frameworks; the paper's output is an interpretive mapping of the literature rather than a closed self-referential chain. This matches the default case of a self-contained external analysis with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on the transferability of human-team psychological taxonomies to human-AI settings and on the representativeness of the 53-paper sample; no free parameters or invented physical entities are introduced.

axioms (1)
  • domain assumption Psychological taxonomies developed for human-only teams apply without major revision to human-AI teams
    Invoked when the authors state they categorise papers 'based on psychological taxonomies of teaming'

pith-pipeline@v0.9.1-grok · 5681 in / 1248 out tokens · 22286 ms · 2026-07-03T06:02:32.477417+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

75 extracted references · 75 canonical work pages · 1 internal anchor

  1. [1]

    Berretta, S.et al.Defining human-ai teaming the human-centered way: a scop- ing review and network analysis.Frontiers in Artificial Intelligence6, 1250725 (2023)

    work page 2023

  2. [2]

    & Buehler, P

    Bienefeld, N., Kolbe, M., Camen, G., Huser, D. & Buehler, P. K. Human- ai teaming: leveraging transactive memory and speaking up for enhanced team effectiveness.Frontiers in Psychology14, 1208019 (2023)

    work page 2023

  3. [3]

    & Wang, X

    Lu, X., Fan, S., Houghton, J., Wang, L. & Wang, X. Readingquizmaker: a human- nlp collaborative system that supports instructors to design high-quality reading quiz questions.Proceedings of the 2023 CHI Conference on Human Factors in Computing Systemsp. 1–18 (2023)

    work page 2023

  4. [4]

    Flathmann, C.et al.Examining the impact of varying levels of ai teammate influence on human-ai teams.International Journal of Human-Computer Studies 177, 103061 (2023)

    work page 2023

  5. [5]

    1–6 (2020)

    Wang, D.et al.From human-human collaboration to human-ai collaboration: Designing ai systems that can work together with people.Extended abstracts of the 2020 CHI conference on human factors in computing systemsp. 1–6 (2020)

    work page 2020

  6. [6]

    & Mosler, E

    ˇCartolovni, A., Tomiˇ ci´ c, A. & Mosler, E. L. Ethical, legal, and social considera- tions of ai-based medical decision-support tools: a scoping review.International Journal of Medical Informatics161, 104738 (2022). 29

    work page 2022

  7. [7]

    L.et al.Task types and team-level attributes: Synthesis of team classification literature.Human resource development review11, 97–129 (2012)

    Wildman, J. L.et al.Task types and team-level attributes: Synthesis of team classification literature.Human resource development review11, 97–129 (2012)

    work page 2012

  8. [8]

    J., Demir, M., Cooke, N

    McNeese, N. J., Demir, M., Cooke, N. J. & Myers, C. Teaming with a synthetic teammate: Insights into human-autonomy teaming.Human factors60, 262–273 (2018)

    work page 2018

  9. [9]

    Krippendorff, K.Content analysis: An introduction to its methodology(Sage publications, 2018)

    work page 2018

  10. [10]

    J., Freeman, G

    Zhang, R., McNeese, N. J., Freeman, G. & Musick, G. ” an ideal human” expectations of ai teammates in human-ai teaming.Proceedings of the ACM on Human-Computer Interaction4, 1–25 (2021)

    work page 2021

  11. [11]

    & Schelble, B

    O’neill, T., McNeese, N., Barron, A. & Schelble, B. Human–autonomy teaming: A review and analysis of the empirical literature.Human factors64, 904–938 (2022)

    work page 2022

  12. [12]

    Salas, E., Cooke, N. J. & Rosen, M. A. On teams, teamwork, and team performance: Discoveries and developments.Human factors50, 540–547 (2008)

    work page 2008

  13. [13]

    P., Keplinger, K

    Smith, A., van Wagoner, H. P., Keplinger, K. & Celebi, C. Navigating ai conver- gence in human–artificial intelligence teams: A signaling theory approach.Journal of Organizational Behavior(2025)

    work page 2025

  14. [14]

    Duan, W.et al.Understanding the evolvement of trust over time within human-ai teams.Proceedings of the ACM on Human-Computer Interaction8, 1–31 (2024)

    work page 2024

  15. [15]

    & Gao, Z

    Gao, Q., Xu, W., Shen, M. & Gao, Z. Agent teaming situation awareness (atsa): A situation awareness framework for human-ai teaming.arXiv preprint arXiv:2308.16785(2023)

    work page arXiv 2023

  16. [16]

    J., Hauptman, A

    Flathmann, C., Duan, W., Mcneese, N. J., Hauptman, A. & Zhang, R. Empirically understanding the potential impacts and process of social influence in human-ai teams.Proceedings of the ACM on Human-Computer Interaction8, 1–32 (2024)

    work page 2024

  17. [17]

    & Satzger, G

    Hemmer, P., Schemmer, M., K¨ uhl, N., V¨ ossing, M. & Satzger, G. Complementar- ity in human-ai collaboration: Concept, sources, and evidence.European Journal of Information Systems34, 979–1002 (2025)

    work page 2025

  18. [18]

    & Bassetto, S

    Cabour, G., Morales-Forero, A., Ledoux, ´E. & Bassetto, S. An explanation space to align user studies with the technical development of explainable ai.AI & SOCIETY38, 869–887 (2023)

    work page 2023

  19. [19]

    Zhang, R.et al.Investigating ai teammate communication strategies and their impact in human-ai teams for effective teamwork.Proceedings of the ACM on Human-Computer Interaction7, 1–31 (2023). 30

    work page 2023

  20. [20]

    & Sitterle, V

    Loper, M. & Sitterle, V. Evolving lvc to include evaluation of human-ai teaming dynamics.2023 Winter Simulation Conference (WSC)p. 2506–2517 (2023)

    work page 2023

  21. [21]

    & Wiebel-Herboth, C

    Attig, C., Wollstadt, P., Schrills, T., Franke, T. & Wiebel-Herboth, C. B. More than task performance: Developing new criteria for successful human-ai teaming using the cooperative card game hanabi.Extended abstracts of the chi conference on human factors in computing systemsp. 1–11 (2024)

    work page 2024

  22. [22]

    J., Duenser, A., Lacey, J

    McGrath, M. J., Duenser, A., Lacey, J. & Paris, C. Collaborative human-ai trust (chai-t): A process framework for active management of trust in human-ai collaboration.Computers in Human Behavior: Artificial Humans100200 (2025)

    work page 2025

  23. [23]

    G., Zhang, R

    Flathmann, C., Schelble, B. G., Zhang, R. & McNeese, N. J. Modeling and guiding the creation of ethical human-ai teams.Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Societyp. 469–479 (2021)

    work page 2021

  24. [24]

    Munn, Z.et al.Systematic review or scoping review? guidance for authors when choosing between a systematic or scoping review approach.BMC medical research methodology18, 1–7 (2018)

    work page 2018

  25. [25]

    C.et al.Prisma extension for scoping reviews (prisma-scr): checklist and explanation.Annals of internal medicine169, 467–473 (2018)

    Tricco, A. C.et al.Prisma extension for scoping reviews (prisma-scr): checklist and explanation.Annals of internal medicine169, 467–473 (2018)

    work page 2018

  26. [26]

    & O’malley, L

    Arksey, H. & O’malley, L. Scoping studies: towards a methodological framework. International journal of social research methodology8, 19–32 (2005)

    work page 2005

  27. [27]

    Bansal, G.et al.A case for backward compatibility for human-ai teams.arXiv preprint arXiv:1906.01148(2019)

    work page internal anchor Pith review Pith/arXiv arXiv 1906

  28. [28]

    J., Houmanfar, R

    Olla, R., Hand, E., Louis, S. J., Houmanfar, R. & Sengupta, S. A cybersecurity game to probe human-ai teaming.2024 IEEE Conference on Games (CoG)p. 1–5 (2024)

    work page 2024

  29. [29]

    & Fouse, A

    Amresh, A., Cooke, N. & Fouse, A. A minecraft based simulated task environment for human ai teaming.Proceedings of the 23rd ACM international conference on intelligent virtual agentsp. 1–3 (2023)

    work page 2023

  30. [30]

    1–6 (2022)

    Schwalb, J.et al.A study of drone-based ai for enhanced human-ai trust and informed decision making in human-ai interactive virtual environments.2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS)p. 1–6 (2022)

    work page 2022

  31. [31]

    B., Nepal, S

    Tariq, S., Chhetri, M. B., Nepal, S. & Paris, C. A2c: A modular multi-stage collab- orative decision framework for human–ai teams.Expert systems with applications 282, 127318 (2025). 31

    work page 2025

  32. [32]

    & Oliva, A

    Josephs, E., Fosco, C. & Oliva, A. Artifact magnification on deepfake videos increases human detection and subjective confidence.arXiv preprint arXiv:2304.04733(2023)

    work page arXiv 2023

  33. [33]

    & Chuah, T

    Ong, C., McGee, K. & Chuah, T. L. Closing the human-ai team-mate gap: how changes to displayed information impact player behavior towards computer teammates.Proceedings of the 24th Australian Computer-Human Interaction Conferencep. 433–439 (2012)

    work page 2012

  34. [34]

    & Riedl, C

    Zvelebilova, J., Savage, S. & Riedl, C. Collective attention in human-ai teams. arXiv preprint arXiv:2407.17489(2024)

    work page arXiv 2024

  35. [35]

    & H¨ ollerer, T

    Xu, C., Lien, K.-C. & H¨ ollerer, T. Comparing zealous and restrained ai recom- mendations in a real-world human-ai collaboration task.Proceedings of the 2023 CHI Conference on Human Factors in Computing Systemsp. 1–15 (2023)

    work page 2023

  36. [36]

    & Chen, B

    Zhang, L., Ji, Z. & Chen, B. Crew: Facilitating human-ai teaming research.arXiv preprint arXiv:2408.00170(2024)

    work page arXiv 2024

  37. [37]

    & Pan, Q

    Munyaka, I., Ashktorab, Z., Dugan, C., Johnson, J. & Pan, Q. Decision making strategies and team efficacy in human-ai teams.Proceedings of the ACM on Human-Computer Interaction7, 1–24 (2023)

    work page 2023

  38. [38]

    A., Lu, Z

    Mahmood, S. A., Lu, Z. & Yin, M. Designing behavior-aware ai to improve the human-ai team performance in ai-assisted decision making.International Joint Conferences on Artificial Intelligence Organization(2024)

    work page 2024

  39. [39]

    255–281 (2019)

    Newn, J.et al.Designing interactions with intention-aware gaze-enabled artificial agents.IFIP Conference on Human-Computer Interactionp. 255–281 (2019)

    work page 2019

  40. [40]

    1–16 (2021)

    Bansal, G.et al.Does the whole exceed its parts? the effect of ai explanations on complementary team performance.Proceedings of the 2021 CHI conference on human factors in computing systemsp. 1–16 (2021)

    work page 2021

  41. [41]

    C., Jonker, C

    Jorge, C. C., Jonker, C. M. & Tielman, M. L. How should an ai trust its human teammates? exploring possible cues of artificial trust.ACM Transactions on Interactive Intelligent Systems14, 1–26 (2024)

    work page 2024

  42. [42]

    & Rogerson, M

    Sidji, M., Smith, W. & Rogerson, M. J. Human-ai collaboration in cooperative games: A study of playing codenames with an llm assistant.Proceedings of the ACM on Human-Computer Interaction8, 1–25 (2024)

    work page 2024

  43. [43]

    S.et al.Human-ai collaboration in real-world complex environment with reinforcement learning.Neural Computing and Applications37, 18957– 18987 (2025)

    Islam, M. S.et al.Human-ai collaboration in real-world complex environment with reinforcement learning.Neural Computing and Applications37, 18957– 18987 (2025). 32

    work page 2025

  44. [44]

    Momose, K., Mehta, R., Moukpe, J., Weekes, T. R. & Eskridge, T. C. Human-ai teamwork interface design using patterns of interactions.International Journal of Human–Computer Interaction41, 7112–7134 (2025)

    work page 2025

  45. [45]

    i’m afraid i can’t do that, dave

    Schadd, M. P., Schoonderwoerd, T. A., van den Bosch, K., Visker, O. H. & Haije, T. “i’m afraid i can’t do that, dave”; getting to know your buddies in a human– agent team.Systems10, 15 (2022)

    work page 2022

  46. [46]

    & Kambhampati, S

    Bhambri, S., Verma, M., Biswas, U., Murthy, A. & Kambhampati, S. Incorpo- rating human flexibility through reward preferences in human-ai teaming.arXiv preprint arXiv:2312.14292(2023)

    work page arXiv 2023

  47. [47]

    & Singh, A

    Ye, W., Bullo, F., Friedkin, N. & Singh, A. K. Modeling human-ai team decision making.arXiv preprint arXiv:2201.02759(2022)

    work page arXiv 2022

  48. [48]

    Li, M., Kamaraj, A. V. & Lee, J. D. Modeling trust dimensions and dynamics in human-agent conversation: A trajectory epistemic network analysis approach. International Journal of Human–Computer Interaction40, 3571–3582 (2024)

    work page 2024

  49. [49]

    Zhang, S.et al.Mutual theory of mind in human-ai collaboration: An empirical study with llm-driven ai agents in a real-time shared workspace task.arXiv preprint arXiv:2409.08811(2024)

    work page arXiv 2024

  50. [50]

    Navigating virtual teams in generative ai-led learning: The moder- ation of team perceived virtuality.Education and Information Technologies29, 23225–23248 (2024)

    Darban, M. Navigating virtual teams in generative ai-led learning: The moder- ation of team perceived virtuality.Education and Information Technologies29, 23225–23248 (2024)

    work page 2024

  51. [51]

    & Weller, A

    Babbar, V., Bhatt, U. & Weller, A. On the utility of prediction sets in human-ai teams.arXiv preprint arXiv:2205.01411(2022)

    work page arXiv 2022

  52. [52]

    & Wies, M

    W¨ urfel, J., Papenfuß, A. & Wies, M. Operationalizing ai explainability using interpretability cues in the cockpit: Insights from user-centered development of the intelligent pilot advisory system (ipas).International Conference on Human- Computer Interactionp. 297–315 (2024)

    work page 2024

  53. [53]

    Prieto Santos, L. P.et al.Single-case learning analytics: Feasibility of a human- centered analytics approach to support doctoral education.JUCS-Journal of Universal Computer Science29, 1033–1068 (2023)

    work page 2023

  54. [54]

    M., Larson, L

    Harris-Watson, A. M., Larson, L. E., Lauharatanahirun, N., DeChurch, L. A. & Contractor, N. S. Social perception in human-ai teams: Warmth and competence predict receptivity to ai teammates.Computers in Human Behavior145, 107765 (2023)

    work page 2023

  55. [55]

    & Cabitza, F

    Milella, F., Natali, C., Scantamburlo, T., Campagner, A. & Cabitza, F. The impact of gender and personality in human-ai teaming: The case of collaborative 33 question answering.IFIP Conference on Human-Computer Interactionp. 329– 349 (2023)

    work page 2023

  56. [56]

    Mallick, R.et al.The pursuit of happiness: the power and influence of ai teammate emotion in human-ai teamwork.Behaviour & Information Technology43, 3436– 3460 (2024)

    work page 2024

  57. [57]

    & Admoni, H

    Zhao, M., Simmons, R. & Admoni, H. The role of adaptation in collective human– ai teaming.Topics in cognitive science17, 291–323 (2025)

    work page 2025

  58. [58]

    G.et al.Towards ethical ai: Empirically investigating dimensions of ai ethics, trust repair, and performance in human-ai teaming.Human Factors 66, 1037–1055 (2024)

    Schelble, B. G.et al.Towards ethical ai: Empirically investigating dimensions of ai ethics, trust repair, and performance in human-ai teaming.Human Factors 66, 1037–1055 (2024)

    work page 2024

  59. [59]

    & Zhang, J

    Jia, S., Li, Z., Chen, N. & Zhang, J. Towards visual explainable active learning for zero-shot classification.IEEE Transactions on Visualization and Computer Graphics28, 791–801 (2021)

    work page 2021

  60. [60]

    & De Laat, M

    Marrone, R., Zamecnik, A., Joksimovic, S., Johnson, J. & De Laat, M. Under- standing student perceptions of artificial intelligence as a teammate.Technology, Knowledge and Learning30, 1847–1869 (2025)

    work page 2025

  61. [61]

    I., Schelble, B

    Hauptman, A. I., Schelble, B. G., Duan, W., Flathmann, C. & McNeese, N. J. Understanding the influence of ai autonomy on ai explainability levels in human- ai teams using a mixed methods approach.Cognition, Technology & Work26, 435–455 (2024)

    work page 2024

  62. [62]

    Duan, W.et al.Understanding the processes of trust and distrust contagion in human–ai teams: A qualitative approach.Computers in Human Behavior165, 108560 (2025)

    work page 2025

  63. [63]

    2429–2437 (2019)

    Bansal, G.et al.Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff.Proceedings of the AAAI conference on artificial intelligence33, p. 2429–2437 (2019)

    work page 2019

  64. [64]

    Zhang, R.et al.Verbal vs. visual: How humans perceive and collaborate with ai teammates using different communication modalities in various human-ai team compositions.Proceedings of the ACM on human-computer Interaction8, 1–34 (2024)

    work page 2024

  65. [65]

    & Isenberg, T

    Hong, J., Maciejewski, R., Trubuil, A. & Isenberg, T. Visualizing and comparing machine learning predictions to improve human-ai teaming on the example of cell lineage.IEEE Transactions on Visualization and Computer Graphics30, 1956–1969 (2023). 34

    work page 1956

  66. [66]

    Mallick, R., Flathmann, C., Duan, W., Schelble, B. G. & McNeese, N. J. What you say vs what you do: Utilizing positive emotional expressions to relay ai team- mate intent within human–ai teams.International Journal of Human-Computer Studies192, 103355 (2024)

    work page 2024

  67. [67]

    J., Schelble, B

    McNeese, N. J., Schelble, B. G., Canonico, L. B. & Demir, M. Who/what is my teammate? team composition considerations in human–ai teaming.IEEE Transactions on Human-Machine Systems51, 288–299 (2021)

    work page 2021

  68. [68]

    & Ulfert, A.-S

    Georganta, E. & Ulfert, A.-S. Would you trust an ai team member? team trust in human–ai teams.Journal of occupational and organizational psychology97, 1212–1241 (2024)

    work page 2024

  69. [69]

    Zhang, Q., Lee, M. L. & Carter, S. You complete me: Human-ai teams and complementary expertise.Proceedings of the 2022 CHI conference on human factors in computing systemsp. 1–28 (2022)

    work page 2022

  70. [70]

    & de Jong, S

    Erengin, T., Briker, R. & de Jong, S. B. You, me, and the ai: The role of third- party human teammates for trust formation toward ai teammates.Journal of Organizational Behavior(2024)

    work page 2024

  71. [71]

    & Zhang, Y

    Lou, B., Lu, T., Raghu, T. & Zhang, Y. Unraveling human-ai teaming: a review and outlook.arXiv preprint arXiv:2504.05755(2025)

    work page arXiv 2025

  72. [72]

    Habli, I.et al.The big argument for ai safety cases.arXiv preprint arXiv:2503.11705(2025)

    work page arXiv 2025

  73. [73]

    Porter, Z.et al.Unravelling responsibility for ai.Journal of Responsible Technology100124 (2025)

    work page 2025

  74. [74]

    The responsibility gap: Ascribing responsibility for the actions of learning automata.Ethics and information technology6, 175–183 (2004)

    Matthias, A. The responsibility gap: Ascribing responsibility for the actions of learning automata.Ethics and information technology6, 175–183 (2004)

    work page 2004

  75. [75]

    & Zwart, S

    Royakkers, L. & Zwart, S. D.Moral responsibility and the problem of many hands (Routledge, 2015). 35 8 Appendix Cluster Paper Application Type Task Type Team Makeup Task interdependence Role structure Leadership structure Communication structure Physical distribution Team life span1 [64] Generic/Theoretical Teamwork Games 1 human 2 AI; 2 human 1 AI Intens...

    work page 2015